Hybrid AI-Physics Framework for Post-Earthquake Structural Damage Diagnosis with Sparse Sensing

Abstract

Rapid and reliable post-earthquake damage assessment is critical for public safety, re-occupancy decisions, and effective emergency response. This paper presents a physics-informed, unsupervised learning framework that enables structural damage diagnosis in sparsely instrumented buildings following seismic events. The approach fuses real sensor data with physics-based simulations to create a hybrid spatiotemporal input grid, extending observability to regions without sensors. A Spatiotemporal Composite Autoencoder Network (SCAN) processes this hybrid input, learning the structure's undamaged behavior from pre-event or ambient-condition data alone. SCAN integrates convolutional layers for extracting localized spatial features and LSTM layers for modeling temporal dynamics, enabling it to recognize deviations from normal behavior caused by damage. Post-event sensor data are analyzed through the trained model, and anomalies are flagged based on elevated reconstruction and prediction errors. These error patterns are spatially mapped to localize potential damage, even in uninstrumented areas. By embedding low-fidelity physical estimates directly into the input representation, the framework enhances detection sensitivity while requiring no labeled damage examples. This hybrid AI-physics approach offers a scalable, interpretable, and data-efficient solution for real-time post-earthquake structural diagnostics, providing critical decision support for emergency managers and accelerating safe and targeted recovery efforts.